As semiconductor technology continues to inch closer to practical limitations in terms of increases in clock speed, architects are increasingly focusing on parallelism in processor architectures to obtain performance improvements. At the chip level, multiple processor cores are often disposed on the same chip, functioning in much the same manner as separate processor chips, or to some extent, as completely separate computers. In addition, even within cores, parallelism is employed through the use of multiple execution units that are specialized to handle certain types of operations. Pipelining is also employed in many instances so that certain operations that may take multiple clock cycles to perform are broken up into stages, enabling other operations to be started prior to completion of earlier operations. Multithreading is also employed to enable multiple instruction streams to be processed in parallel, enabling more overall work to performed in any given clock cycle.
One area where parallelism continues to be exploited is in the area of execution units, e.g., fixed point or floating point execution units. Many floating point execution units, for example, are deeply pipelined. However, while pipelining can improve performance, pipelining is most efficient when the instructions processed by a pipeline are not dependent on one another, e.g., where a later instruction does not use the result of an earlier instruction. Whenever an instruction operates on the result of another instruction, typically the later instruction cannot enter the pipeline until the earlier instruction has exited the pipeline and calculated its result. The later instruction is said to be dependent on the earlier instruction, and phenomenon of stalling the later instruction waiting for the result of an earlier instruction is said to introduce “bubbles,” or cycles where no productive operations are being performed, into the pipeline.
One technique that may be used to extract higher utilization from a pipelined execution unit and remove unused bubbles is to introduce multithreading. In this way, other threads are able to issue instructions into the unused slots in the pipeline, which drives the utilization and hence the aggregate throughput up. Another popular technique for increasing performance is to use a single instruction multiple data (SIMD) architecture, which is also referred to as ‘vectorizing’ the data. In this manner, operations are performed on multiple data elements at the same time, and in response to the same SIMD instruction. A vector execution unit typically includes multiple processing lanes that handle different datapoints in a vector and perform similar operations on all of the datapoints at the same time. For example, for an architecture that relies on quad(4)word vectors, a vector execution unit may include four processing lanes that perform the identical operations on the four words in each vector. The aforementioned techniques may also be combined, resulting in a multithreaded vector execution unit architecture that enables multiple threads to issue SIMD instructions to a vector execution unit to process “vectors” of data points at the same time. In addition, multiple execution units may be used to permit independent operations to be performed in parallel, further increasing overall performance.
The various techniques for improving execution unit performance, however, do not come without a cost. Parallelism adds complexity, often requiring a greater number of logic gates, which increases both the size and the power consumption of such execution units. Coupling these techniques with the general desire to increase performance through other techniques, such as increased switching frequency, the power consumption of complex, high performance execution units continues to increase, despite efforts to reduce such power consumption through process improvements. Excessive power consumption can present issues for portable or battery powered devices, but more typically, excessive power consumption presents issues for nearly all electronic circuits due to the generation of heat, which often requires elaborate cooling systems to ensure that a circuit does not overheat and fail.
Chip-wide control over power consumption is often used in electronic circuits such as those used in laptop computers or other portable devices, typically by throttling down the clock rate or frequency of the circuit to reduce power consumption and the generation of heat. In addition, power consumption may also be reduced in some instances by temporarily shutting down unused circuits on a chip. In all of these instances, however, throttling back the power consumption of the circuit usually results in lower performance in the chip. Furthermore, the circuit characteristics that define the overall power consumption of such circuits, e.g., cycle time, voltage, logic area, capacitance, etc., are most often designed to meet a maximum performance target.
It has been found, however, that not all tasks performed by electronic devices require maximum performance. For example, some modern multithreaded processors implement multithreading as well as multiple parallel execution units that are optimized for maximum performance, and permit multiple instructions to be handled per clock cycle in the parallel execution units. The software that runs on such processors, however, is often broken into multiple program tasks that can execute in parallel on the multiple hardware threads. Some of these tasks, however, do not require quick turn-around on high performance execution units since other more critical program tasks may play a much greater role in limiting overall performance. Many background tasks, for example, are not critical to overall performance, and could be completed at a later time without impacting overall system performance.
For background and other non-time sensitive tasks, execution of these tasks with a high performance execution unit is often overkill, and needlessly adds to the power consumption and heat generation of the processor.